class_docstr: HRFPN (High Resolution Feature Pyrmamids)
  
  paper: `High-Resolution Representations for Labeling Pixels and Regions
  <https://arxiv.org/abs/1904.04514>`_.
  
  Args:
      in_channels (list): number of channels for each branch.
      out_channels (int): output channels of feature pyramids.
      num_outs (int): number of output stages.
      pooling_type (str): pooling for generating feature pyramids
          from {MAX, AVG}.
      conv_cfg (dict): dictionary to construct and config conv layer.
      norm_cfg (dict): dictionary to construct and config norm layer.
      with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed.
      stride (int): stride of 3x3 convolutional layers
docstr: Initialize the weights of module.
docstr_tok: ["Initialize","the","weights","of","module","."]
code: def init_weights(self):
      """Initialize the weights of module."""
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              caffe2_xavier_init(m)
  
file_path: insightface/detection/scrfd/mmdet/models/necks/hrfpn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, caffe2_xavier_init
  from torch.utils.checkpoint import checkpoint
  from ..builder import NECKS
label_desc: Use image node_id 2 for calling the HRFPN obj's underlying member method code with example usage: obj.init_weights() without return types